Overview

Dataset statistics

Number of variables12
Number of observations576
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory187.3 KiB
Average record size in memory333.0 B

Variable types

Numeric8
Categorical4

Alerts

Visits Jul 21 is highly overall correlated with Total VisitsHigh correlation
Amount Paid Jul 21 is highly overall correlated with Amount Paid Jul 22 and 1 other fieldsHigh correlation
Visits Jul 22 is highly overall correlated with Amount Paid Jul 22 and 1 other fieldsHigh correlation
Amount Paid Jul 22 is highly overall correlated with Amount Paid Jul 21 and 2 other fieldsHigh correlation
MedicalExpense is highly overall correlated with Amount Paid Jul 21 and 2 other fieldsHigh correlation
Total Visits is highly overall correlated with Visits Jul 21 and 2 other fieldsHigh correlation
S/n is uniformly distributedUniform
S/n has unique valuesUnique

Reproduction

Analysis started2023-04-22 20:14:00.237759
Analysis finished2023-04-22 20:14:26.764213
Duration26.53 seconds
Software versionversion 1.0.0
Download configurationconfig.json

Variables

S/n
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct576
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.52778
Minimum1
Maximum606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-04-22T23:14:26.967271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29.75
Q1152.75
median303.5
Q3453.25
95-th percentile574.25
Maximum606
Range605
Interquartile range (IQR)300.5

Descriptive statistics

Standard deviation174.64075
Coefficient of variation (CV)0.57727177
Kurtosis-1.1991006
Mean302.52778
Median Absolute Deviation (MAD)150.5
Skewness-0.012700739
Sum174256
Variance30499.391
MonotonicityStrictly increasing
2023-04-22T23:14:27.344924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
2 1
 
0.2%
402 1
 
0.2%
403 1
 
0.2%
404 1
 
0.2%
405 1
 
0.2%
406 1
 
0.2%
407 1
 
0.2%
408 1
 
0.2%
409 1
 
0.2%
Other values (566) 566
98.3%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
606 1
0.2%
604 1
0.2%
603 1
0.2%
601 1
0.2%
600 1
0.2%
599 1
0.2%
598 1
0.2%
597 1
0.2%
596 1
0.2%
594 1
0.2%

Gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Male
312 
Female
264 

Length

Max length6
Median length4
Mean length4.9166667
Min length4

Characters and Unicode

Total characters2832
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 312
54.2%
Female 264
45.8%

Length

2023-04-22T23:14:27.906338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T23:14:28.329698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 312
54.2%
female 264
45.8%

Most occurring characters

ValueCountFrequency (%)
e 840
29.7%
a 576
20.3%
l 576
20.3%
M 312
 
11.0%
F 264
 
9.3%
m 264
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2256
79.7%
Uppercase Letter 576
 
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 840
37.2%
a 576
25.5%
l 576
25.5%
m 264
 
11.7%
Uppercase Letter
ValueCountFrequency (%)
M 312
54.2%
F 264
45.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2832
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 840
29.7%
a 576
20.3%
l 576
20.3%
M 312
 
11.0%
F 264
 
9.3%
m 264
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 840
29.7%
a 576
20.3%
l 576
20.3%
M 312
 
11.0%
F 264
 
9.3%
m 264
 
9.3%

Age
Real number (ℝ)

Distinct15
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9166667
Minimum3
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-04-22T23:14:28.535377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median7
Q310
95-th percentile16
Maximum17
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.544009
Coefficient of variation (CV)0.4476643
Kurtosis-0.044024309
Mean7.9166667
Median Absolute Deviation (MAD)2
Skewness0.91577934
Sum4560
Variance12.56
MonotonicityNot monotonic
2023-04-22T23:14:28.905146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 103
17.9%
4 82
14.2%
6 78
13.5%
9 52
9.0%
7 52
9.0%
8 48
8.3%
10 31
 
5.4%
11 28
 
4.9%
12 22
 
3.8%
13 21
 
3.6%
Other values (5) 59
10.2%
ValueCountFrequency (%)
3 3
 
0.5%
4 82
14.2%
5 103
17.9%
6 78
13.5%
7 52
9.0%
8 48
8.3%
9 52
9.0%
10 31
 
5.4%
11 28
 
4.9%
12 22
 
3.8%
ValueCountFrequency (%)
17 15
 
2.6%
16 16
 
2.8%
15 8
 
1.4%
14 17
 
3.0%
13 21
3.6%
12 22
3.8%
11 28
4.9%
10 31
5.4%
9 52
9.0%
8 48
8.3%

Category
Categorical

Distinct9
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size43.8 KiB
Dispensary
119 
Specialized Clinic (Polyclinic)
118 
District Hospital
109 
Zonal Referral Hospital
86 
Regional Referral Hospital
79 
Other values (4)
65 

Length

Max length31
Median length23
Mean length20.638889
Min length8

Characters and Unicode

Total characters11888
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpecialized Clinic (Polyclinic)
2nd rowSpecialized Clinic (Polyclinic)
3rd rowHealth Centre
4th rowDispensary
5th rowZonal Referral Hospital

Common Values

ValueCountFrequency (%)
Dispensary 119
20.7%
Specialized Clinic (Polyclinic) 118
20.5%
District Hospital 109
18.9%
Zonal Referral Hospital 86
14.9%
Regional Referral Hospital 79
13.7%
National Referral Hospital 24
 
4.2%
Health Centre 23
 
4.0%
Pharmacy 10
 
1.7%
Specialized Clinics 8
 
1.4%

Length

2023-04-22T23:14:29.412023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T23:14:29.727920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
hospital 298
22.4%
referral 189
14.2%
specialized 126
9.5%
dispensary 119
 
8.9%
clinic 118
 
8.9%
polyclinic 118
 
8.9%
district 109
 
8.2%
zonal 86
 
6.5%
regional 79
 
5.9%
national 24
 
1.8%
Other values (4) 64
 
4.8%

Most occurring characters

ValueCountFrequency (%)
i 1478
12.4%
l 1187
 
10.0%
a 988
 
8.3%
e 897
 
7.5%
754
 
6.3%
s 653
 
5.5%
r 639
 
5.4%
c 607
 
5.1%
o 605
 
5.1%
t 586
 
4.9%
Other values (19) 3494
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9568
80.5%
Uppercase Letter 1330
 
11.2%
Space Separator 754
 
6.3%
Open Punctuation 118
 
1.0%
Close Punctuation 118
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1478
15.4%
l 1187
12.4%
a 988
10.3%
e 897
9.4%
s 653
6.8%
r 639
6.7%
c 607
6.3%
o 605
6.3%
t 586
 
6.1%
n 575
 
6.0%
Other values (8) 1353
14.1%
Uppercase Letter
ValueCountFrequency (%)
H 321
24.1%
R 268
20.2%
D 228
17.1%
C 149
11.2%
P 128
 
9.6%
S 126
 
9.5%
Z 86
 
6.5%
N 24
 
1.8%
Space Separator
ValueCountFrequency (%)
754
100.0%
Open Punctuation
ValueCountFrequency (%)
( 118
100.0%
Close Punctuation
ValueCountFrequency (%)
) 118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10898
91.7%
Common 990
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1478
13.6%
l 1187
10.9%
a 988
 
9.1%
e 897
 
8.2%
s 653
 
6.0%
r 639
 
5.9%
c 607
 
5.6%
o 605
 
5.6%
t 586
 
5.4%
n 575
 
5.3%
Other values (16) 2683
24.6%
Common
ValueCountFrequency (%)
754
76.2%
( 118
 
11.9%
) 118
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1478
12.4%
l 1187
 
10.0%
a 988
 
8.3%
e 897
 
7.5%
754
 
6.3%
s 653
 
5.5%
r 639
 
5.4%
c 607
 
5.1%
o 605
 
5.1%
t 586
 
4.9%
Other values (19) 3494
29.4%

Ownership
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size36.6 KiB
Private
442 
Faith Based
100 
Government
 
34

Length

Max length11
Median length7
Mean length7.8715278
Min length7

Characters and Unicode

Total characters4534
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate
2nd rowPrivate
3rd rowFaith Based
4th rowPrivate
5th rowFaith Based

Common Values

ValueCountFrequency (%)
Private 442
76.7%
Faith Based 100
 
17.4%
Government 34
 
5.9%

Length

2023-04-22T23:14:30.140786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T23:14:30.460180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
private 442
65.4%
faith 100
 
14.8%
based 100
 
14.8%
government 34
 
5.0%

Most occurring characters

ValueCountFrequency (%)
a 642
14.2%
e 610
13.5%
t 576
12.7%
i 542
12.0%
v 476
10.5%
r 476
10.5%
P 442
9.7%
B 100
 
2.2%
d 100
 
2.2%
s 100
 
2.2%
Other values (7) 470
10.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3758
82.9%
Uppercase Letter 676
 
14.9%
Space Separator 100
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 642
17.1%
e 610
16.2%
t 576
15.3%
i 542
14.4%
v 476
12.7%
r 476
12.7%
d 100
 
2.7%
s 100
 
2.7%
h 100
 
2.7%
n 68
 
1.8%
Other values (2) 68
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
P 442
65.4%
B 100
 
14.8%
F 100
 
14.8%
G 34
 
5.0%
Space Separator
ValueCountFrequency (%)
100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4434
97.8%
Common 100
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 642
14.5%
e 610
13.8%
t 576
13.0%
i 542
12.2%
v 476
10.7%
r 476
10.7%
P 442
10.0%
B 100
 
2.3%
d 100
 
2.3%
s 100
 
2.3%
Other values (6) 370
8.3%
Common
ValueCountFrequency (%)
100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 642
14.2%
e 610
13.5%
t 576
12.7%
i 542
12.0%
v 476
10.5%
r 476
10.5%
P 442
9.7%
B 100
 
2.2%
d 100
 
2.2%
s 100
 
2.2%
Other values (7) 470
10.4%

Region
Categorical

Distinct24
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
Kinondoni
233 
Temeke
68 
Ilala
57 
Mwanza
45 
Kilimanjaro
31 
Other values (19)
142 

Length

Max length11
Median length9
Mean length7.3263889
Min length4

Characters and Unicode

Total characters4220
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.5%

Sample

1st rowTemeke
2nd rowIlala
3rd rowMorogoro
4th rowUnguja
5th rowKilimanjaro

Common Values

ValueCountFrequency (%)
Kinondoni 233
40.5%
Temeke 68
 
11.8%
Ilala 57
 
9.9%
Mwanza 45
 
7.8%
Kilimanjaro 31
 
5.4%
Unguja 26
 
4.5%
Geita 22
 
3.8%
Mbeya 19
 
3.3%
Arusha 11
 
1.9%
Pwani 10
 
1.7%
Other values (14) 54
 
9.4%

Length

2023-04-22T23:14:30.798789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kinondoni 233
40.5%
temeke 68
 
11.8%
ilala 57
 
9.9%
mwanza 45
 
7.8%
kilimanjaro 31
 
5.4%
unguja 26
 
4.5%
geita 22
 
3.8%
mbeya 19
 
3.3%
arusha 11
 
1.9%
pwani 10
 
1.7%
Other values (14) 54
 
9.4%

Most occurring characters

ValueCountFrequency (%)
n 837
19.8%
i 577
13.7%
o 557
13.2%
a 415
9.8%
K 269
 
6.4%
e 254
 
6.0%
d 241
 
5.7%
l 145
 
3.4%
m 109
 
2.6%
T 83
 
2.0%
Other values (23) 733
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3644
86.4%
Uppercase Letter 576
 
13.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 837
23.0%
i 577
15.8%
o 557
15.3%
a 415
11.4%
e 254
 
7.0%
d 241
 
6.6%
l 145
 
4.0%
m 109
 
3.0%
r 74
 
2.0%
k 69
 
1.9%
Other values (11) 366
10.0%
Uppercase Letter
ValueCountFrequency (%)
K 269
46.7%
T 83
 
14.4%
M 74
 
12.8%
I 62
 
10.8%
U 26
 
4.5%
G 22
 
3.8%
A 11
 
1.9%
P 10
 
1.7%
S 9
 
1.6%
D 5
 
0.9%
Other values (2) 5
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4220
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 837
19.8%
i 577
13.7%
o 557
13.2%
a 415
9.8%
K 269
 
6.4%
e 254
 
6.0%
d 241
 
5.7%
l 145
 
3.4%
m 109
 
2.6%
T 83
 
2.0%
Other values (23) 733
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 837
19.8%
i 577
13.7%
o 557
13.2%
a 415
9.8%
K 269
 
6.4%
e 254
 
6.0%
d 241
 
5.7%
l 145
 
3.4%
m 109
 
2.6%
T 83
 
2.0%
Other values (23) 733
17.4%

Visits Jul 21
Real number (ℝ)

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.328125
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-04-22T23:14:31.092607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78350869
Coefficient of variation (CV)0.58993596
Kurtosis25.237724
Mean1.328125
Median Absolute Deviation (MAD)0
Skewness4.1841688
Sum765
Variance0.61388587
MonotonicityNot monotonic
2023-04-22T23:14:31.313671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 444
77.1%
2 102
 
17.7%
3 17
 
3.0%
4 7
 
1.2%
5 3
 
0.5%
8 2
 
0.3%
7 1
 
0.2%
ValueCountFrequency (%)
1 444
77.1%
2 102
 
17.7%
3 17
 
3.0%
4 7
 
1.2%
5 3
 
0.5%
7 1
 
0.2%
8 2
 
0.3%
ValueCountFrequency (%)
8 2
 
0.3%
7 1
 
0.2%
5 3
 
0.5%
4 7
 
1.2%
3 17
 
3.0%
2 102
 
17.7%
1 444
77.1%

Amount Paid Jul 21
Real number (ℝ)

Distinct428
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36415.174
Minimum1000
Maximum775000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-04-22T23:14:31.662015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile5075
Q110937.5
median20130
Q335000
95-th percentile96662.5
Maximum775000
Range774000
Interquartile range (IQR)24062.5

Descriptive statistics

Standard deviation70229.907
Coefficient of variation (CV)1.9285891
Kurtosis49.510713
Mean36415.174
Median Absolute Deviation (MAD)10750
Skewness6.3791121
Sum20975140
Variance4.9322399 × 109
MonotonicityNot monotonic
2023-04-22T23:14:31.949744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17000 8
 
1.4%
7000 6
 
1.0%
29000 5
 
0.9%
8950 5
 
0.9%
8000 5
 
0.9%
21000 5
 
0.9%
35000 4
 
0.7%
10000 4
 
0.7%
22000 4
 
0.7%
19500 4
 
0.7%
Other values (418) 526
91.3%
ValueCountFrequency (%)
1000 1
0.2%
1400 1
0.2%
1500 1
0.2%
2000 1
0.2%
2250 1
0.2%
2500 1
0.2%
2600 1
0.2%
2900 1
0.2%
3000 1
0.2%
3120 1
0.2%
ValueCountFrequency (%)
775000 1
0.2%
653250 1
0.2%
649600 1
0.2%
522240 1
0.2%
435160 1
0.2%
374720 1
0.2%
374300 1
0.2%
357200 1
0.2%
325570 1
0.2%
315020 1
0.2%

Visits Jul 22
Real number (ℝ)

Distinct8
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4947917
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-04-22T23:14:32.284589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum17
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1556924
Coefficient of variation (CV)0.77314616
Kurtosis66.551774
Mean1.4947917
Median Absolute Deviation (MAD)0
Skewness6.375838
Sum861
Variance1.335625
MonotonicityNot monotonic
2023-04-22T23:14:33.029596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 405
70.3%
2 110
 
19.1%
3 40
 
6.9%
4 14
 
2.4%
5 3
 
0.5%
9 2
 
0.3%
10 1
 
0.2%
17 1
 
0.2%
ValueCountFrequency (%)
1 405
70.3%
2 110
 
19.1%
3 40
 
6.9%
4 14
 
2.4%
5 3
 
0.5%
9 2
 
0.3%
10 1
 
0.2%
17 1
 
0.2%
ValueCountFrequency (%)
17 1
 
0.2%
10 1
 
0.2%
9 2
 
0.3%
5 3
 
0.5%
4 14
 
2.4%
3 40
 
6.9%
2 110
 
19.1%
1 405
70.3%

Amount Paid Jul 22
Real number (ℝ)

Distinct456
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35672.422
Minimum1000
Maximum597220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-04-22T23:14:33.632988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile5671.25
Q110795
median21475
Q341000
95-th percentile108120
Maximum597220
Range596220
Interquartile range (IQR)30205

Descriptive statistics

Standard deviation51382.401
Coefficient of variation (CV)1.4403957
Kurtosis44.859557
Mean35672.422
Median Absolute Deviation (MAD)12045
Skewness5.6590324
Sum20547315
Variance2.6401512 × 109
MonotonicityNot monotonic
2023-04-22T23:14:33.916476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 6
 
1.0%
9600 6
 
1.0%
11600 5
 
0.9%
10000 5
 
0.9%
33200 4
 
0.7%
39000 4
 
0.7%
7000 4
 
0.7%
24100 3
 
0.5%
32500 3
 
0.5%
16000 3
 
0.5%
Other values (446) 533
92.5%
ValueCountFrequency (%)
1000 1
0.2%
2000 1
0.2%
3000 2
0.3%
3500 1
0.2%
3600 1
0.2%
3610 1
0.2%
3900 1
0.2%
3950 1
0.2%
4000 2
0.3%
4100 1
0.2%
ValueCountFrequency (%)
597220 1
0.2%
489925 1
0.2%
416005 1
0.2%
338445 1
0.2%
331110 1
0.2%
251070 1
0.2%
248000 2
0.3%
221050 1
0.2%
217500 1
0.2%
193390 1
0.2%

MedicalExpense
Real number (ℝ)

Distinct541
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72087.595
Minimum5000
Maximum800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-04-22T23:14:34.332532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile13990
Q124370
median46925
Q380210
95-th percentile212762.5
Maximum800000
Range795000
Interquartile range (IQR)55840

Descriptive statistics

Standard deviation92399.047
Coefficient of variation (CV)1.2817607
Kurtosis21.50151
Mean72087.595
Median Absolute Deviation (MAD)24360
Skewness4.1223596
Sum41522455
Variance8.5375838 × 109
MonotonicityNot monotonic
2023-04-22T23:14:34.649261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30200 3
 
0.5%
18450 3
 
0.5%
37600 3
 
0.5%
40550 2
 
0.3%
15900 2
 
0.3%
102200 2
 
0.3%
113050 2
 
0.3%
41700 2
 
0.3%
60000 2
 
0.3%
37700 2
 
0.3%
Other values (531) 553
96.0%
ValueCountFrequency (%)
5000 1
0.2%
8650 1
0.2%
9180 1
0.2%
9250 1
0.2%
9450 1
0.2%
10000 1
0.2%
10100 1
0.2%
10200 1
0.2%
10390 1
0.2%
10430 1
0.2%
ValueCountFrequency (%)
800000 1
0.2%
733400 1
0.2%
709550 1
0.2%
643620 1
0.2%
530040 1
0.2%
525925 1
0.2%
482200 1
0.2%
463110 1
0.2%
453525 1
0.2%
435000 1
0.2%

Total Visits
Real number (ℝ)

Distinct13
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8229167
Minimum2
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-04-22T23:14:34.916936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q33
95-th percentile5
Maximum19
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5162525
Coefficient of variation (CV)0.53712267
Kurtosis33.304484
Mean2.8229167
Median Absolute Deviation (MAD)0
Skewness4.6156669
Sum1626
Variance2.2990217
MonotonicityNot monotonic
2023-04-22T23:14:35.126040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 323
56.1%
3 148
25.7%
4 60
 
10.4%
5 26
 
4.5%
6 9
 
1.6%
11 2
 
0.3%
12 2
 
0.3%
13 1
 
0.2%
9 1
 
0.2%
8 1
 
0.2%
Other values (3) 3
 
0.5%
ValueCountFrequency (%)
2 323
56.1%
3 148
25.7%
4 60
 
10.4%
5 26
 
4.5%
6 9
 
1.6%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
11 2
 
0.3%
ValueCountFrequency (%)
19 1
 
0.2%
13 1
 
0.2%
12 2
 
0.3%
11 2
 
0.3%
10 1
 
0.2%
9 1
 
0.2%
8 1
 
0.2%
7 1
 
0.2%
6 9
 
1.6%
5 26
4.5%

Interactions

2023-04-22T23:14:22.847758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:01.546159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:04.134319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:06.504848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:09.411711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:13.509416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:16.512144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:19.065394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:23.348585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:01.913670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:04.442423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:06.826049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:10.729369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:13.862987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:16.798510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:19.371436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:23.653104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:02.252335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:04.691216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:07.160236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:11.112793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:14.281157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:17.080079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:19.662650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:24.044561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:02.569646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:04.998266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:07.500330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:11.481590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:14.808035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:17.398231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:20.081774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:24.490847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:02.903084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:05.270169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:08.004837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:11.859745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:15.159033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:17.767361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:20.527641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:24.841542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:03.182298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:05.579302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:08.347446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:12.211887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:15.518964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:18.065858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:21.247909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:25.175747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:03.481979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:05.835426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:08.696529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:12.762664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:15.794101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:18.352027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:21.998572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:25.500006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:03.833500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:06.221776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:09.070589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:13.169896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:16.210771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:18.719953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-22T23:14:22.398191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-22T23:14:35.384064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
S/nAgeVisits Jul 21Amount Paid Jul 21Visits Jul 22Amount Paid Jul 22MedicalExpenseTotal VisitsGenderCategoryOwnershipRegion
S/n1.0000.254-0.0000.0440.0690.0860.0780.0470.0000.0430.0000.059
Age0.2541.0000.022-0.019-0.056-0.021-0.015-0.0380.0170.0450.0000.093
Visits Jul 21-0.0000.0221.0000.5000.1190.1490.3810.6540.0000.0890.1490.000
Amount Paid Jul 210.044-0.0190.5001.0000.0670.5050.8250.3360.0000.1150.2190.000
Visits Jul 220.069-0.0560.1190.0671.0000.5280.3760.7810.0000.0000.0530.000
Amount Paid Jul 220.086-0.0210.1490.5050.5281.0000.8660.4660.0000.1360.0000.000
MedicalExpense0.078-0.0150.3810.8250.3760.8661.0000.5120.0000.1780.1680.000
Total Visits0.047-0.0380.6540.3360.7810.4660.5121.0000.0590.0480.1120.000
Gender0.0000.0170.0000.0000.0000.0000.0000.0591.0000.0000.0000.000
Category0.0430.0450.0890.1150.0000.1360.1780.0480.0001.0000.4120.374
Ownership0.0000.0000.1490.2190.0530.0000.1680.1120.0000.4121.0000.439
Region0.0590.0930.0000.0000.0000.0000.0000.0000.0000.3740.4391.000

Missing values

2023-04-22T23:14:25.963131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-22T23:14:26.537531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

S/nGenderAgeCategoryOwnershipRegionVisits Jul 21Amount Paid Jul 21Visits Jul 22Amount Paid Jul 22MedicalExpenseTotal Visits
01Female4Specialized Clinic (Polyclinic)PrivateTemeke128000111600396002
12Male4Specialized Clinic (Polyclinic)PrivateIlala131950246960789103
23Male4Health CentreFaith BasedMorogoro153501390092502
34Male4DispensaryPrivateUnguja110100318460285604
45Male9Zonal Referral HospitalFaith BasedKilimanjaro2610003497001107005
56Male4Specialized Clinic (Polyclinic)PrivateKinondoni17000112660196602
67Male16Regional Referral HospitalPrivateGeita274380114130885103
78Male5District HospitalFaith BasedTanga228200115000432003
89Male16Specialized Clinic (Polyclinic)PrivateKinondoni119500127960474602
910Female4Regional Referral HospitalPrivateKinondoni31047601260001307604
S/nGenderAgeCategoryOwnershipRegionVisits Jul 21Amount Paid Jul 21Visits Jul 22Amount Paid Jul 22MedicalExpenseTotal Visits
566594Male11District HospitalPrivateTemeke17380234150415303
567596Male7National Referral HospitalGovernmentKinondoni127000127500545002
568597Female5District HospitalPrivateTemeke121600111500331002
569598Male7District HospitalPrivateTemeke212900228800417004
570599Male4Regional Referral HospitalPrivateKinondoni138450115180536302
571600Female11DispensaryPrivateUnguja11003016000160302
572601Male15Specialized Clinic (Polyclinic)PrivateArusha23020021420001722004
573603Female8Zonal Referral HospitalPrivateKinondoni12070031129501336504
574604Male10Regional Referral HospitalPrivateKinondoni1700031363101433104
575606Male6National Referral HospitalFaith BasedKinondoni2429502802501232004